genetic algorithm

In this paper, the multi-disciplinary design optimization of a re-entry bio-capsule configuration is performed. In this process, all design disciplines related to the configuration and structural objectives, such as minimizing the structure's deformation, maximizing the structure's first natural frequency, and maximizing the buckling load multiplier of the structure, are considered. Therefore, the design disciplines selected include geometry, aerodynamics, trajectory, heating, and structure. In this regard, considering the design space defined by the allowable limits of the geometric variables of the bio-capsule, surrogate models are extracted using the combinatorial Kriging-Response Surface Method (RSM). After modeling the design disciplines and preparing the surrogate models, the optimal design point is identified using the Genetic Algorithm (GA). To solve this problem, the multi-objective All-At-Once (AAO) framework is used. The optimization approaches of the problem include mass minimization, CDA parameter maximization, volumetric efficiency maximization, ballistic coefficient minimization, static longitudinal stability maximization, structural deformation minimization, internal volume maximization, first natural frequency maximization, and buckling load multiplier maximization. The results showed that using the combinatorial surrogate model method significantly improves the accuracy of the surrogate models in the optimization process (accuracy reaches more than 90%). Finally, the different configurations obtained by the present method were compared with the flight test results of the native bio-capsule configuration, which showed a favorable match in all flight path parameters of the capsules.

In recent years, due to the optimal geometry and lower pressure drop, diffusion control vanes have significant applications, especially in the aviation industry and in subsonic and transsonic conditions. In the current research, the airfoil of the axial stator compressor section designed in the National Aerospace Laboratory of India has been selected as the basic geometry. The goal of optimization is to minimize the pressure drop of the entire fluid flow and consequently reduce the drop rate. The working method in this research is the change in the profile geometry of the blade by changing the parameters of the parsec method, which leads to the creation of new geometries at each stage of the code execution. The used optimization method is developed based on Genetic Algorithm. For the aerodynamic analysis of the generated geometry in each step and extracting the total pressure drop value, the MATLAB code is coupled with Ansys software and in each step, after numerical solution for each generated geometry, the total pressure drop value is extracted and returned to the code. Finally, the work output of the vane is more optimal and with a lower pressure drop, which is finally compared with the original vane and introduced as a suitable alternative. The total pressure drop between inlet and outlet in the optimized vane has decreased by 18% compared to the original vane, and the mass flow rate has also increased by 0.083 kg/s, which is a significant amount. The improvement of various aerodynamic characteristics such as Mach number distribution and pressure and drop coefficients can also be seen between the two basic and optimized blades, which is detailed at the end of the article

The aim of this paper is to obtain an optimized profile for the manufacturing of a thermoplastic PE80 pipe to achieve the lowest possible weight per pipe unit while maintaining an allowable amount of diameter variation according to ISO 9969 standard. In this paper, the behavior of the material will be analyzed using the FEM (Finite Element Analysis) method in Ansys Mechanical software. The types of pipes studied (in terms of geometry) are corrugated and spiral corrugated pipes, and the goal is to obtain an optimized profile with varying geometric conditions that results in the least weight while adhering to the constraints set by the standard. To precisely find the optimized profile, a very high number of runs from the dataset is required, which would be costly; therefore, in this paper, with a selection of initial runs chosen by the experimental design method, a neural network is trained for the weight function so that it can be directly studied as a function. This way, instead of adjusting and solving the Ansys model each time to extract the weight of the dataset or the deformation under loading, the function can be executed in a fraction of a second, providing the values instantly. The optimization algorithm used is a genetic algorithm, which utilizes the developed neural network functions and obtains the optimal weight conditions under the deformation constraints of the standard.

One of the challenges in cubesat with the dimensions of three units and smaller is the optimization of electrical energy consumption. This can be achieved by optimizing the execution time of the main mods to complete the mission. In this article, using the genetic algorithm optimization tool, an optimal function for the duration of the detumbling operational mode - which plays the most important role in energy consumption due to its long duration - is presented, according to which the energy consumption will be minimized for the mission. By estimating the duration of the detumbling mode according to the initial speed of the satellite, a suitable estimate of the total duration of the satellite mission scenario is available, and with the aim of minimizing the energy, the mission can be executed at the right time (running the anti-jamming mode and then the targeting mode). This method will be implemented in three steps: in the first step, according to different initial angular velocity conditions, the detumbling mode will be implemented and its time data will be collected. In the second step, using the available data bank and genetic optimization algorithm, the best function will be fitted to the data bank. Finally, in the third step, this function will be executed and the results will be checked and analyzed.

In this article, the simulation and integrated control of flight-propulsion are addressed as a significant and necessary research topic in the aerospace industry. In previous research, flight control and turbine engine control have often been treated separately; however, the mutual effects of flight dynamics and turbine engine dynamics on one another have received less attention. Initially, to model the movement of the UAV body, kinematic and flight dynamics equations are presented. Transmission equations are developed based on Newton’s laws, and rotational equations are derived from Euler’s theory. Subsequently, the thermodynamic and dynamic equations necessary for turbojet engine modeling are introduced. The equations governing the engine inlet opening, compressor, turbine, combustion chamber, and nozzle are examined separately. All simulations are conducted in MATLAB/Simulink. Flight graphic is displayed in real-time using FlightGear software. A structure for simulating and controlling flight dynamics and engine dynamics is proposed, addressing not only the control of flight and the UAV’s trajectory but also the control of the shaft’s rotational speed while observing the limitations of the turbojet engine. Additionally, flight-propulsion control coefficients are simultaneously optimized for a specific mission using genetic algorithm. Finally, the simulation results are presented and discussed.

A dynamic vibration absorber is a system with a simple structure including mass, spring and damper, which is added to the main system to reduce the range of vibrations and prevent the phenomenon of resonance. In this research, a dynamic double vibration absorber is proposed to reduce the torsional vibrations of a rotor. For this purpose, at first, the governing equations of the system are derived by the Lagrange method, and then the torsional vibrations of the system are calculated. The genetic algorithm is used to calculate the optimal parameters of the dynamic vibration absorber in resonance mode. In order to check the performance of the absorber, the results were compared with one of the previous researches, and then the range of torsional vibrations of the system in different frequency ratios for the states without absorber, with the optimal double absorber with zero damping and the optimal double absorber with damping were investigated. Then, the dimensionless vibration amplitude diagram of the system at different frequency ratios, at different distances of the absorber placement from the center of the rotor disk, is examined. Finally, the range of dimensionless vibrations of the system is investigated in the ratio of different masses of absorbers and it is concluded that increasing or decreasing the ratio of mass of absorbers compared to the optimal value increases the range of vibrations of the system in resonance mode and frequencies close to it.

This article focuses on the derivation of nonlinear dynamic equations and form-finding using genetic algorithms for class 1 tensegrity structure. The structures under consideration feature a triangular cross-section, with a sphere enclosing them. The nonlinear dynamic equations of the system are obtained by applying the Lagrangian approach and the finite element method, considering the nodal positions as the generalized coordinates. The proposed approach illustrates how to develop large-scale, detailed dynamic models with different tensegrity structures.  The form-finding approach employs a simple framework capable of identifying both regular and irregular tensegrity configurations without limitation on dimensions. Stable tensegrity structures are created by applying specific restrictions to random configurations. The genetic algorithm and multi-objective functions are used to determine the fitness function and create these structures. Three separate scenarios with both defined and random connection matrices and member types—bars and cables—evaluate the performance of the proposed method for arbitrary architectures. The resulting models are validated with regard to force density. The vibration behavior of the final models under harmonic loads is investigated via modal analysis. The simulation results demonstrate the efficacy of the proposed method in precisely determining the vibration characteristics of tensegrity structures through the application of an intelligent form-finding methodology. This method is capable of managing both regular and irregular tensegrity structures in stochastic conditions. This approach is capable of handling both regular and irregular tensegrity structures in stochastic conditions.

Today, with the advancement of technology, improvement and quality of life, artificial intelligence has found a special place in the world. In this research, the optimization of the curved mesh structure made of polylactic acid has been discussed. Mesh structures are widely used in various industries due to their advantages such as high energy absorption. The geometric parameters of this structure can have a noticeable effect on the amount of energy absorption of these structures. In this regard, optimization of geometrical parameters has been done using genetic algorithm. In this research, the parameters of radius of curvature R1, angle of curvature Ɵ1, radius of curvature R2, angle of curvature Ɵ2, length L relative to the energy absorbed by the structure, maximum force and modulus of elasticity have been optimized. A large number of optimization parameters confirm the use of genetic algorithm for the optimization process of this structure. Optimization using genetic algorithm requires the existence of an objective function to which the geometric parameters are optimized. This objective function should be a continuous function that can produce the necessary outputs for each geometric parameter. In this research, artificial neural network has been used to construct the objective function. The artificial neural network makes it possible to create a continuous function with a limited number of inputs and outputs that can produce suitable outputs for receiving different inputs. After extracting the optimal geometric parameters, the optimal structure was made using a 3D printer and subjected to quasi-static pressure testing.

The purpose of this article is to maximize the aerodynamic efficiency of an airplane during flight in the vicinity of the sea waves. A whole 3D aircraft flight at the closed proximity to the wavy sea water has been numerically simulated by the Fluent commercial software and the results were used to train a neural network. This network was then employed to determine the maximum aerodynamic efficiency in the vicinity of sea waves. The combination of the computational fluid dynamics and the neural network has approved that the flight must be performed at a certain speed to attain the maximum aerodynamic efficiency with a respect to a varied range of sea waves amplitude. The results can be considered as the inputs for autopilot system to provide the most efficient flight over the sea waves.

This study focuses on the investigation of intelligent form-finding and vibration analysis of a triangular polyhedral tensegrity that is enclosed within a sphere and subjected to external loads. The nonlinear dynamic equations of the system are derived using the Lagrangian approach and the finite element method. The proposed form-finding approach, which is based on a basic genetic algorithm, can determine regular or irregular tensegrity shapes without dimensional constraints. Stable tensegrity structures are generated from random configurations and based on defined constraints (nodes located on the sphere, parallelism, and area of upper and lower surfaces), and shape finding is performed using the fitness function of the genetic algorithm and multi-objective optimization goals. The genetic algorithm's efficacy in determining the shape of structures with unpredictable configurations is evaluated in two distinct scenarios: one involving a known connection matrix and the other involving fixed or random member positions (struts and cables). The shapes obtained from the algorithm suggested in this study are validated using the force density approach, and their vibration characteristics are examined. The findings of the comparative study demonstrate the efficacy of the proposed methodology in determining the vibrational behavior of tensegrity structures through the utilization of intelligent shape seeking techniques.

Nonlinear buckling caused by grain boundaries in two-dimensional structures and geometric defects are introduced as factors affecting the fracture behavior of these structures. Since the mechanical behavior of polycrystalline nanostructures is not well known, in this article, the mechanical behavior of carbon nitride nanosheets was studied as a function of temperature and grain boundaries. The mechanical performance of polycrystalline carbon nitride was tested in the presence and absence of edge cracks and at temperatures from 100 to 900 K. Molecular dynamics simulation was used as a cost-effective method for modeling two-dimensional nanosheets by choosing the appropriate potential function and boundary conditions. The results showed that the mechanical properties of carbon nitride monocrystalline decrease with increasing temperature and the results were reported in zigzag direction higher than armchair. The same trend was observed for the polycrystalline structures. The increase in temperature caused the mechanical features to decrease. Also, increasing the crack length from 5 to 25 angstroms caused a decrease in mechanical properties. On the other hand, increasing the number of regions for carbon nitride polycrystalline decreased the failure stress. In addition, the optimization was carried out by genetic algorithm and the results showed that the optimal value of Young's modulus for carbon nitride polycrystalline with 53 regions at a temperature of 586.95 K and a crack length of 6.52 angstroms is equivalent to 338.18 GPa. The results obtained from this study can be generalized to more complex cases to predict a deeper understanding of the next generations of two-dimensional structures.

There is no determined method for Micro Air Vehicles (MAVs) design (unlike full-scale aircraft), so MAVs design is very complex and vague. For this reason, the design of MAVs is very expensive (time-consuming), and finally, the obtained design could not be more optimal. To solve these challenges, this study developed a framework for Multidisciplinary Design Optimization (MDO) of fixed-wing MAVs. This framework aims to use the benefits of MDO (time reduction and achieving optimal design) in the design process of MAVs. So, it is tried to consider the most important modules for analysis, and the framework can consider all flight phases in the design optimization process. Geometry, weight, the center of gravity, aerodynamics, and power are the considered modules in this framework. The analysis of all modules is performed for the entire flight phase. To show the performance of this framework, the design optimization of a fixed-wing MAV has been done by considering take-off weight and drag as objective functions.  The considered constraints for this research are from stability and geometry modules. It is worth noting that with attention to the complex design space of MAVs and the capability of the Genetic Algorithm (GA), this algorithm has been considered as an optimization algorithm in this study.

Social robots that are fabricated to interact with humans and to help them in education, healthcare, etc., are required to have an interactive behavior similar to humans. One of the important interactive behaviors of humans is social eye gaze. Eye gaze is significantly more important than other nonverbal signals; it is shown that eyes are special cognitive stimuli with unique hardwired pathways in the brain dedicated to their interpretation. Studying the literature, we found out that in previous research conducted to control the social robots’ gaze behavior, human gaze behavior was investigated in some limited situations, such as two- or three-way conversation, in order to extract the pattern of this behavior. Therefore, increasing the variety of studied social situations is a way to fill this gap. In order to design a gaze control system for a social robot, details about human gaze behavior must be found. The purpose of this research is to propose an empirical motion-time pattern for human gaze behavior in a number of different social situations; these situations include scenes with 2 to 4 people in a prepared video where the people in the scene show the social behaviors of "talking", "waving", "pointing", "entering the scene" and "exiting the scene" in a structured way. Fifteen normal adults (mean age: 24 and std: 3.3 years) watched this movie, and their gaze positions were recorded using an eye tracker system (SR-Research EyeLink 1000 plus). Next, by using the genetic algorithm (which is an optimization process), we were able to extract the relative coefficient of each of the mentioned social behaviors in our proposed model. The results of reconstructing the participants' gaze on the test data are very similar to the real performance of the subjects. Finally, the ability to implement this model was successfully tested by implementing it on a Nao robot, and its positive performance was confirmed using a survey. The model showed significant differences between the two studied situations in 3 questions out of the whole survey’s 10 questions.

Optimization of composite shafts subjected to torsional loading has been investigated in the previous studies by considering the constant value of load and so, minimization of shaft mass was defined as the objective function (OF). In the current study, maximization of the torque to mass (T/m) ratio was considered as OF. To do so, the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) methods were utilized. The number of layers, thickness and angle of each ply as well as the applied torque were considered as the input variables. Moreover, preventing of failure in composite shaft, based on Tsai-Wu failure theory developed in Abaqus finite element software, was defined as the constraint of optimization problem. Also, in order to investigate the effect of OF type, in addition to the T/m, the mass was also defined as OF in a separate optimization problem. The results revealed that despite PSO, GA had suitable convergence in the optimization. Moreover, in spite of the type of OF, using a composite shaft compared to the steel one, had at least 80% mass reduction. Furthermore, although the predicted composite shaft via T/m OF has more mass compared to that predicted via m OF, it can tolerate torsional loading up to 8.5 times more. This point can increase the load carrying capacity of composite shaft.

The current research has two main parts, the first part is the process of design, manufacture and selection of quadrotor's elements and the second part is modeling and estimation of model parameters based on flight test data. The main approach of making this drone is to be light and cheap. Also, it is very useful to develop a precise mathematical model for the quadrotor to control the system. Therefore, a system identification method for the quadrotor model based on genetic algorithm and neural network is investigated in the second part. The advantage of system identification based on experimental data is designing a better controller. Using flight test data including angles and rate of them, the identification of the system based on the two methods of genetic algorithm and neural network has been done. For genetic algorithm modeling, a linear second order function is considered for each channel. Meanwhile, it is assumed that the channels are decoupled from each other. The results show that in the roll axis, the results of the identification are the same, in the pitch axis, the accuracy of identification with the second order function and the genetic algorithm method has better results, but in the yaw axis, the accuracy of identification with the neural network is significantly better. In general, the adaptability in all three axes is 81.48% for the genetic algorithm method and 82.62% for the neural network algorithm method.

The subject of helical springs, which are vital and widely used components in various industries, particularly gains significantly more importance in the aerospace industry. Helical springs are utilized for energy storage purposes in various applications, notably in spacecraft separator mechanisms. However, one of the major challenges in designing these components is reducing their mass, especially in space applications where every gram carries substantial significance. This paper delves into optimizing the mass of helical springs used in spacecraft separator mechanisms. Initially, we solve the mass optimization problem of helical springs using conventional mathematical equations and the genetic algorithm. Subsequently, we design a new model for the springs using artificial neural networks and conduct optimization using this new model. The obtained results demonstrate that using the proposed artificial neural network model offers considerable advantages over the initial method. This optimization using the proposed model enhances the accuracy of helical spring designs. Numerical results indicate that the output in the neural network related to shear stress is 88.545, differing by 0.83% from the finite element method, and the output related to deformation in the neural network is 41.76, differing by 0.58% from the finite element method. These findings underscore the significantly improved performance of the proposed model compared to previous methods.

One of the most important issues related to the power supply of TWTA lamps in satellites is to have low ripple, high efficiency, high reliability and optimal volume and weight,. In this article, the efficiency and reliability of high voltage DC/DC electronic-power converter is optimized for use in satellite and TWTA lamps. The goal of optimization using multi-objective genetic algorithm (NSGA-II) in this article is to minimize the objective function, which includes efficiency and reliability. Markov model is used to evaluate reliability, in which short-circuit and open-circuit errors are considered for circuit switches and diodes, and short-circuit errors are considered for passive circuit elements. for optimization, first, the input variables of the algorithm are determined as the input of the objective function, so that with the help of sensitivity analysis, the parameters that have low sensitivity and their changes do not have a major impact on the objective function are eliminated. parameters of NSGA-II algorithm, including the number of iterations, the number of populations, and the probability have been determined for the accurate calculation of circuit variables. the results section this method, in addition to maintaining high efficiency, with the optimal selection of elements, high reliability can be achieved .